Detection of Early-Stage Pancreatic Ductal Adenocarcinoma From Blood Samples: Results of a Multiplex Biomarker Signature Validation Study.

INTRODUCTION: The IMMray PanCan-d test combines an 8-plex biomarker signature with CA19-9 in a proprietary algorithm to detect pancreatic ductal adenocarcinoma (PDAC) in serum samples. This study aimed to validate the clinical performance of the IMMray PanCan-d test and to better understand test performance in Lewis-null (le/le) individuals who cannot express CA19-9. METHODS: Serum samples from 586 individuals were analyzed with the IMMray PanCan-d biomarker signature and CA19-9 assay, including 167 PDAC samples, 203 individuals at high risk of familial/hereditary PDAC, and 216 healthy controls. Samples were collected at 11 sites in the United States and Europe. The study was performed by Immunovia, Inc (Marlborough, MA), and sample identity was blinded throughout the study. Test results were automatically generated using validated custom software with a locked algorithm and predefined decision value cutoffs for sample classification. RESULTS: The IMMray PanCan-d test distinguished PDAC stages I and II (n = 56) vs high-risk individuals with 98% specificity and 85% sensitivity and distinguished PDAC stages I-IV vs high-risk individuals with 98% specificity and 87% sensitivity. We identified samples with a CA19-9 value of 2.5 U/mL or less as probable Lewis-null (le/le) individuals. Excluding these 55 samples from the analysis increased the IMMray PanCan-d test sensitivity to 92% for PDAC stages I-IV (n = 157) vs controls (n = 379) while maintaining specificity at 99%; test sensitivity for PDAC stages I and II increased from 85% to 89%. DISCUSSION: These results demonstrate the IMMray PanCan-d blood test can detect PDAC with high specificity (99%) and sensitivity (92%).

Impact of hemolysis on multi-OMIC pancreatic biomarker discovery to derisk biomarker development in precision medicine studies.

Cancer biomarker discovery is critically dependent on the integrity of biofluid and tissue samples acquired from study participants. Multi-omic profiling of candidate protein, lipid, and metabolite biomarkers is confounded by timing and fasting status of sample collection, participant demographics and treatment exposures of the study population. Contamination by hemoglobin, whether caused by hemolysis during sample preparation or underlying red cell fragility, contributes 0-10 g/L of extraneous protein to plasma, serum, and Buffy coat samples and may interfere with biomarker detection and validation. We analyzed 617 plasma, 701 serum, and 657 buffy coat samples from a 7-year longitudinal multi-omic biomarker discovery program evaluating 400+ participants with or at risk for pancreatic cancer, known as Project Survival. Hemolysis was undetectable in 93.1% of plasma and 95.0% of serum samples, whereas only 37.1% of buffy coat samples were free of contamination by hemoglobin. Regression analysis of multi-omic data demonstrated a statistically significant correlation between hemoglobin concentration and the resulting pattern of analyte detection and concentration. Although hemolysis had the greatest impact on identification and quantitation of the proteome, distinct differentials in metabolomics and lipidomics were also observed and correlated with severity. We conclude that quality control is vital to accurate detection of informative molecular differentials using OMIC technologies and that caution must be exercised to minimize the impact of hemolysis as a factor driving false discovery in large cancer biomarker studies.

Artificial intelligence to assess body composition on routine abdominal CT scans and predict mortality in pancreatic cancer- A recipe for your local application.

BACKGROUND: Body composition is associated with mortality; however its routine assessment is too time-consuming. PURPOSE: To demonstrate the value of artificial intelligence (AI) to extract body composition measures from routine studies, we aimed to develop a fully automated AI approach to measure fat and muscles masses, to validate its clinical discriminatory value, and to provide the code, training data and workflow solutions to facilitate its integration into local practice. METHODS: We developed a neural network that quantified the tissue components at the L3 vertebral body level using data from the Liver Tumor Challenge (LiTS) and a pancreatic cancer cohort. We classified sarcopenia using accepted skeletal muscle index cut-offs and visceral fat based its median value. We used Kaplan Meier curves and Cox regression analysis to assess the association between these measures and mortality. RESULTS: Applying the algorithm trained on LiTS data to the local cohort yielded good agreement [>0.8 intraclass correlation (ICC)]; when trained on both datasets, it had excellent agreement (>0.9 ICC). The pancreatic cancer cohort had 136 patients (mean age: 67 ± 11 years; 54% women); 15% had sarcopenia; mean visceral fat was 142 cm2. Concurrent with prior research, we found a significant association between sarcopenia and mortality [mean survival of 15 ± 12 vs. 22 ± 12 (p < 0.05), adjusted HR of 1.58 (95% CI: 1.03-3.33)] but no association between visceral fat and mortality. The detector analysis took 1 ± 0.5 s. CONCLUSIONS: AI body composition analysis can provide meaningful imaging biomarkers from routine exams demonstrating AI's ability to further enhance the clinical value of radiology reports.

Use of ring-enhancement and focal necrosis to differentiate pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma on CT and MRI.

PURPOSE: To assess the ability of the ring-enhancing sign and focal necrosis to diagnose adenosquamous carcinoma (ASqC), a variant of pancreatic ductal adenocarcinoma (PDAC), on MRI and CT. METHODS: The following features of ASqC and conventional PDAC were evaluated on CT and MRI: tumor size, location, margins, borders (non-exophytic, exophytic), and T1 signal intensity. Two readers, blinded to histopathology results, rated their confidence in detecting ring-enhancement and focal necrosis (FN) on a 5-point Likert scale on both MRI and CT. Inter-reader agreement was assessed with Cohen's kappa (k). RESULTS: A total of 24 patients were included: eight patients with treatment naïve and histologically proven ASqC (six women, mean age: 63, range: 40-75) and 16 patients with PDAC (eight women, mean age: 67, range: 47-83). Statistically significant differences between ASqC and PDAC were seen in tumor size, location, presence of FN, and ring enhancement (p = 0.01-0.037). The readers were more confident in depicting the key differentiating feature ring-enhancement in ASqC on MRI compared to CT (confidence 1.71 ± 0.49 vs. 0.88 ± 0.35, p = 0.017) with moderate inter-reader agreement (k = 0.46 and 0.5, respectively). FN showed substantial inter-reader agreement on MR and moderate agreement on CT (k = 0.67 and 0.5, respectively). CONCLUSIONS: Compared to CT, MRI depicts ring-enhancement in ASqC with greater reader confidence and FN in ASqC with higher inter-reader agreement. The concurrent presence of these two imaging features should raise high suspicion for ASqC.

Incidental pulmonary embolism in pancreatic ductal adenocarcinoma: Impact of tumor and AJCC stages at initial staging CT.

BACKGROUND/OBJECTIVES: To determine the prevalence of incidental pulmonary embolism (PE) detected during initial staging CT among patients with newly diagnosed pancreatic ductal adenocarcinoma (PDAC) and assess their association with underlying tumor burden. MATERIALS AND METHODS: This retrospective cohort study evaluated staging chest CT scans (2013-2017) to identify PE among patients with treatment naïve, biopsy-proven PDAC. Data included age, sex, T stage, AJCC stage, presence/absence of metastases and their location at diagnosis. The association of PE with tumor (T1-T4) and AJCC stage were assessed using Pearson Chi-square and Fischer's exact test. A threshold p-value of <0.05 indicated statistical significance. RESULTS: A total of 174 patients (90 female, mean age, 68 years; range: 34-93) were identified, of which 10 patients harbored incidental PE (prevalence, 5.7%). In the PE group, two patients presented with distant metastasis (liver, 20%), while eight patients had T4 tumors (80%). No statistical association was detected between PE and age, sex, and the presence/absence or location of distant metastasis (p = 0.065, p = 0.59, p = 0.687 and p = 0.933, respectively). Patients with T4 tumors and higher AJCC stages (stage III/IV) were significantly more likely to present with PE than those with lower T stage (p = 0.045) and AJCC stage (stage I/II; p = 0.017). CONCLUSION: The prevalence of incidental PE among PDAC patients undergoing initial CT staging is 5.7%. Patients with T4 and AJCC stages III/IV are at higher risk of PE. Caution should be exercised during radiographic interpretation of initial staging chest CTs, as incidental PE may be lurking and require treatment.